Microsimulation methods for population projection - article ; n°1 ; vol.10, pg 97-136

Evert Vanimhoff, Wendy Post

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Population - Année 1998 - Volume 10 - Numéro 1 - Pages 97-136
Van Imhoff (Evert), Post (Wendy). - Microsimulation methods for population projections Microsimulation differs from traditional macrosimulation in using a sample rather than the total population, in operating at the level of individual data rather than aggregated data, and in being based on repeated random experiments rather than average numbers. Here are presented the circumstances in which microsimulation can be of greater value than the more conventional methods. It is particularly relevant when the results of the process being studied are complex whereas the forces driving it are simple. A particular problem in microsimulation results from the fact that the projections are subject to random variation. Various sources of random variations are examined but the most important is the one we refer to as specification randomness: the more explanatory variables are included in the model, the greater the degree of random variation affecting the output of the model. After a brief survey of the microsimulation models which exist in demography, a number of the essential characteristics of microsimulation are illustrated using the KINSIM model for projecting the future size and structure of kinship networks.
Van Imhoff (Evert), Post (Wendy). - Méthodes de micro-simulation pour des projections de population La micro-simulation se distingue de la macro-simulation traditionnelle, en utilisant un échantillon plutôt que la population totale, en travaillant au niveau de données individuelles plutôt que de données agrégées, et en se basant sur des expériences aléatoires répétées plutôt que sur des nombres moyens. Nous présentons ici les circonstances sous lesquelles la micro-simulation peut être plus intéressante que des méthodes plus conventionnelles. Elle est particulièrement appropriée si les résultats du processus étudié sont complexes, tandis que les forces qui lui sont sous-jacentes sont simples. Un problème difficile en micro-simulation vient de ce que les projections sont sujettes à des variations aléatoires. Diverses sources d'aléas sont présentées, mais la plus importante est ce que nous appelons l'aléa de spécification : plus on introduit de variables explicatives dans le modèle, plus le degré d'aléa, auquel les sorties du modèle sont sujettes sera important. Après une revue rapide des modèles de micro-simulation qui existent en démographie, plusieurs des caractéristiques essentielles de la micro-simulation sont illustrées avec le modèle KINSIM, pour projeter la taille et la structure des réseaux de parenté futurs.
Van Imhoff (Evert), Post (Wendy). -Métodos de micro-simulación para proyecciones de población Algunos de los elementos que distinguen la micro-simulación de la macro-simula- ción tradicional son: el uso de muestras en lugar de población total, trabajo a nivel de datos individuates en vez de datos agregados y uso de experimentos aleatorios repetidos en lugar de médias. El articulo présenta las condiciones bajo las cuales la micro-simulación puede ser más interesante que los métodos convencionales. La micro-simulación es especialmente apropiada si los resultados del proceso estudiado son complejos mientras que las fuerzas subyacentes son simples. Una de las dificultades existentes en micro-simulación es que las proyecciones están sujetas a variaciones aleatorias. Existen varias fuentes de error, pero el más importante es el derivado de la propia especificación del modelo : cuantas más variables explicativas se introduzcan en el modelo, mayor sera el nivel de error al cual los resultados del modelo están sujetos. Después de realizar una rápida revision de los modelos de micro-simulación que existen en demografia, el articulo ilustra varias caracteristicas esenciales de la micro- simulación a través del modelo KINSIM, para proyectar el tamaňo y la estructura de las re- des de parentesco futuras.
40 pages
Source : Persée ; Ministère de la jeunesse, de l’éducation nationale et de la recherche, Direction de l’enseignement supérieur, Sous-direction des bibliothèques et de la documentation.

Sujets

• Sociologie, société et politique

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• Publié par: POPULATION0
• Publié le: 01 janvier 1998
• Nombre de lectures: 88
• Langue: English
• Poids de l'ouvrage: 3 Mo

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Evert Van Imhoff
Wendy Post
Microsimulation methods for population projection
In: Population, 10e année, n°1, 1998 pp. 97-136.
Citer ce document / Cite this document :
Van Imhoff Evert, Post Wendy. Microsimulation methods for population projection. In: Population, 10e année, n°1, 1998 pp. 97-
136.
http://www.persee.fr/web/revues/home/prescript/article/pop_0032-4663_1998_hos_10_1_6824Abstract
Van Imhoff (Evert), Post (Wendy). - Microsimulation methods for population projections Microsimulation
differs from traditional macrosimulation in using a sample rather than the total population, in operating at
the level of individual data rather than aggregated data, and in being based on repeated random
experiments rather than average numbers. Here are presented the circumstances in which
microsimulation can be of greater value than the more conventional methods. It is particularly relevant
when the results of the process being studied are complex whereas the forces driving it are simple. A
particular problem in microsimulation results from the fact that the projections are subject to random
variation. Various sources of random variations are examined but the most important is the one we refer
to as specification randomness: the more explanatory variables are included in the model, the greater
the degree of random variation affecting the output of the model. After a brief survey of the
microsimulation models which exist in demography, a number of the essential characteristics of are illustrated using the KINSIM model for projecting the future size and structure of
kinship networks.
Résumé
Van Imhoff (Evert), Post (Wendy). - Méthodes de micro-simulation pour des projections de population
La micro-simulation se distingue de la macro-simulation traditionnelle, en utilisant un échantillon plutôt
que la population totale, en travaillant au niveau de données individuelles plutôt que de données
agrégées, et en se basant sur des expériences aléatoires répétées plutôt que sur des nombres moyens.
Nous présentons ici les circonstances sous lesquelles la micro-simulation peut être plus intéressante
que des méthodes plus conventionnelles. Elle est particulièrement appropriée si les résultats du
processus étudié sont complexes, tandis que les forces qui lui sont sous-jacentes sont simples. Un
problème difficile en micro-simulation vient de ce que les projections sont sujettes à des variations
aléatoires. Diverses sources d'aléas sont présentées, mais la plus importante est ce que nous appelons
l'aléa de spécification : plus on introduit de variables explicatives dans le modèle, plus le degré d'aléa,
auquel les sorties du modèle sont sujettes sera important. Après une revue rapide des modèles de
micro-simulation qui existent en démographie, plusieurs des caractéristiques essentielles de la micro-
simulation sont illustrées avec le modèle KINSIM, pour projeter la taille et la structure des réseaux de
parenté futurs.
Resumen
Van Imhoff (Evert), Post (Wendy). -Métodos de micro-simulación para proyecciones de población
Algunos de los elementos que distinguen la de la macro-simula- ción tradicional son:
el uso de muestras en lugar de población total, trabajo a nivel de datos individuates en vez de datos
agregados y uso de experimentos aleatorios repetidos en lugar de médias. El articulo présenta las
condiciones bajo las cuales la micro-simulación puede ser más interesante que los métodos
convencionales. La micro-simulación es especialmente apropiada si los resultados del proceso
estudiado son complejos mientras que las fuerzas subyacentes son simples. Una de las dificultades
existentes en micro-simulación es que las proyecciones están sujetas a variaciones aleatorias. Existen
varias fuentes de error, pero el más importante es el derivado de la propia especificación del modelo :
cuantas más variables explicativas se introduzcan en el modelo, mayor sera el nivel de error al cual los
resultados del modelo están sujetos. Después de realizar una rápida revision de los modelos de micro-
simulación que existen en demografia, el articulo ilustra varias caracteristicas esenciales de la a través del modelo KINSIM, para proyectar el tamaňo y la estructura de las re- des de
parentesco futuras.MICROSIMULATION METHODS
FOR POPULATION PROJECTION
Evert VAN IMHOFF* and Wendy POST**
I. - Introduction
Population projections are almost invariably produced with the so-
called cohort-component method. In its simplest form, this method boils
down to the following. The population is classified by sex (males and fe
males) and age group (cohorts). For each combination of sex s and age x,
the initial population is transformed into a projected final population of
sex s and age x+l by projecting the population changes, distinguished by
type (components). Typical components are mortality and fertility. These
calculations are repeated for successive time intervals, where the final
population of one interval serves as the initial population for the next in
terval, until the end of the projection period has been reached.
The basic idea behind the cohort-component model is that the popul
ation changes because individuals experience certain demographic events,
and that the mechanisms underlying these events differ between the sexes,
age groups, and the type of event. The total number of events of a certain
type, for each combination of age and sex, is projected as the result of
two factors: the size of the population exposed to the risk of experiencing
the event; and the level (or intensity) of the risk for individual persons,
which may be interpreted as a measure of demographic behaviour.
Suppose that we want to project the number of children born during
the year out of 100,000 women aged 25. The population consists of
100,000 women and each 25-year old woman has a probability of 0.10 to
'rate' is 0.10). bear a child during the year (i.e. the age-specific fertility
Now according to the traditional methods of demographic projection, which
might be called macrosimulation, the projected number of births is obtained
by applying the fertility probability to the size of the group of women:
0.10 x 100,000 yields 10,000 projected births.
** * Netherlands Department of Interdisciplinary Medical Statistics, Demographic Faculty of Institute Medicine, (NIDI), Leyden The University, Hague, Netherlands. Netherlands
Population: An English Selection, special issue New Methodological Approaches
in the Social Sciences, 1998, 97-138. 98 E. VAN IMHOFF, W. POST
In contrast, microsimulation would proceed as follows:0 )
— first, a sample of, say, 1,000 women is drawn from the population;
— next, for each woman in the sample, a random experiment is done
with 0.10 probability of success. More specifically, in each experiment a
random number is drawn from the uniform distribution over the (0,1) in
terval. If the number drawn is less than 0.10, the woman is deemed to
have a child. For understandable reasons, this roulette-like procedure is
known as the Monte Carlo technique. On average, 1,000 experiments will
yield 100 successes, i.e. births. However, in a particular model run there
can be either less or more than 100 simulated births;
— finally, the number of births in the sample is scaled to
the population level: 100 births among a sample of 1,000 implies
10,000 births for a population of 100,000.
Now in this particular example, microsimulation is needlessly comp
licated. The projection problem is so trivial that no demographer would
ever user microsimulation to solve it. Nevertheless, the example illustrates
the three essential ingredients of the approach that dis
tinguish it from traditional macrosimulation:
— the model uses a sample rather than the total population;
— it works on the level of individual data rather than grouped data;
— it relies on repeated random experiments rather than on average
fractions.
Together, these three ingredients imply several strengths, as well as
several weaknesses for microsimulation. These strengths and weaknesses
will be discussed extensively in this article, but at the outset it should be
stressed that microsimulation can do certain things that macrosimulation
cannot. For this reason alone, microsimulation should definitely be taken
seriously as a potentially powerful tool for demographic as well as for
non-demographic projection purposes.
The origins of the microsimulation approach go back to the late 1950s
(Orcutt, 1957; Orcutt et ai, 1961). With the advances in computer tech
nology, the method has gained increasing popularity in recent years. Howe
ver, many of the advantages and principles of the approach have been
recognized independently and dispersed over several disciplines (Clarke,
1986). In demography, quite a number of applications of microsimulation
exist today - an overview will be given later on in this paper - but a
coherent literature of the essentials of demographic is
lacking. With this paper, we hope to provide a first attempt in this d